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High expression of the receptor tyrosine kinase (RTK) insulin-like growth factor-1 receptor (IGF1R) and RTK mutations are associated with high-risk/worse prognosis in multiple myeloma (MM). Combining the pIGF1R/pINSR inhibitor linsitinib with the proteasome inhibitor (PI) bortezomib seemed promising in a clinical trial, but IGF1R expression was not associated with therapy response. Because the oncogenic impact of IGF1R mutations is so far unknown, we investigated the functional impact of IGF1R mutations on survival signaling, viability/proliferation and survival response to therapy. We transfected four human myeloma cell lines (HMCLs) with IGF1R^WT, IGF1R^D1146N and IGF1R^N1129S (Sleeping Beauty), generated CRISPR-Cas9 IGF1R knockouts in the HMCLs U-266 (IGF1R^WT) and L-363 (IGF1R^D1146N) and tested the anti-MM activity of linsitinib alone and in combination with the second-generation PI carfilzomib in seven HMCLs. IGF1R knockout entailed reduced proliferation. Upon IGF1R overexpression, survival signaling was moderately increased in all HCMLs and slightly affected by IGF1R^N1129S in one HMCL, whereby the viability remained unaffected. Expression of IGF1R^D1146N reduced pIGF1R-Y1135, especially under serum reduction, but did not impact downstream signaling. Linsitinib and carfilzomib showed enhanced anti-myeloma activity in six out of seven HMCL irrespective of the IGF1R mutation status. In conclusion, IGF1R mutations can impact IGF1R activation and/or downstream signaling, and a combination of linsitinib with carfilzomib might be a suitable therapeutic approach for MM patients potentially responsive to IGF1R blockade. Full article

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities. Full article